Hivemall provides machine learning functionality as well as feature
engineering functions through UDFs/UDAFs/UDTFs of Hive. It is designed
to be scalable to the number of training instances as well as the number
of training features.

Though we consider that Hivemall is much easier to use and more scalable
than Mahout for classification/regression tasks, please check it by
yourself. If you have a Hive environment, you can evaluate Hivemall
within 5 minutes or so.

Hivemall is very easy to use as every machine learning step is done
within HiveQL.

-- Installation is just as follows:add jar /tmp/hivemall.jar;
source /tmp/define-all.hive;

-- Logistic regression is performed by a query.SELECT
feature,
avg(weight) as weight
FROM (
SELECT logress(features,label) as (feature,weight)
FROM training_features
) t
GROUP BY feature;